CDW Canada Tech Talks

What are some of the traditional uses cases for HPC and AI that the Federal Government uses currently?

·        What are some of the identified use cases that have been difficult to implement?

·        What challenges have stalled those implementations historically?

·        How has the current state of HPC and AI changed so that some of those use cases can now be realized?

·        What needs to be done so the federal space can implement the remainder?

·        How have traditional HPC and AI technologies changed and how has that made things easier for implementations?

·        How have things become more complex or challenging?

·        What types of investments are being made for infrastructure to support modern HPC and AI platforms?

·        How has the density of HPC and AI created challenges for IT teams?

·        How are large hosting providers and public cloud providers handling AI at scale challenges?

·        What has the advent of Generative AI done to change the HPC and AI landscape?

·        Let’s talk about deterministic vs probabilistic models.  How are they different?

o   How does this difference make them more suitable to different use cases?

·        When we think about data governance and AI governance it becomes a bit of safety vs innovation.  How do we create a healthy balance between those two? 

·        How can we mitigate risk but still drive forward innovation of AI?

·        As we look to the next 2 to 3 years what are some of the new use cases that you think will drive investment and also provide value?

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• Up until recently access to AI was really restricted to fewer people.  The need for specialized tools, knowledge and technology created hurdles that many organizations were not able to clear.  How have you seen access to AI and the perceived value of AI change across different consumers?
• One of the ways we have been segmenting generative AI with customers has been drawing a distinction between the targeted consumer of the AI tools and the risk profile of the data that data consumer will be accessing.  How has the need for data governance become a barrier for broader AI use cases?
• How has the hype around generative AI changed the types of traditional AI conversations you are having?
• In addition to better establishing more mature data governance practices what are organizations doing to reduce the risk of over sharing data?
• Do organizations view digital sovereignty for AI services differently that other types of more traditional data and application services?
• What does the current distribution of AI workloads on premises, at the edge and in the public cloud look like?
• How sensitive is the data being used for the initial phase of generative AI services we are seeing organizations build today?
• Are some verticals more prepared to take advantage of their data with AI?
• How are organizations deciding between operating on premises vs in the public cloud for AI services?
• Are we able to put the factors that influence the decision-making process in order of importance?
• How do the traditional methods of managing data and technology limit our ability to integrate AI into our organizations quickly and effectively?
• What are some of the ways we can change to adapt to the new demands of AI?
• How can AI development platforms accelerate an organization’s AI journey?
• How does managing AI assets differ from traditional application deployment management?
• How will organizations decide to be a consumer of AI vs a creator of their own AI assets?
• What is the importance of measuring the efficacy of your AI model?  How do you see AI assets being measured for efficacy and compared between iterations?
• As we look to the future what are some of the trends that we predict in this space?
• What excites you about the future of AI and the tools that you will get into the hands of organizations and users?
• How can our listeners become advocates for AI in their organizations and where can they go to learn more to stay on top of all things Ai related?

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What has the role of supercomputers been historically?
Has the advent of GPU clusters changed the role of HPC?
How is the technology changing to meet new needs of researchers and organizations?
As we look at supercomputing across different verticals, what parts of the technology and software stack stay similar and where does that stack depart from being similar?
How do we continue to innovate the technology and software but also address the sprawl of new technology offerings?
What is the role of a manufacturer in supporting open-source tools vs building tools optimized for your own hardware?
How are we managing the cooling challenges more effectively today?
As we move forward, what is the role of supercomputing?
Will we see general access to supercomputing via generative AI like we are seeing with foundation models?
What are some of your favourite supercomputer use cases that you have been involved with?
What excites you about the future of supercomputing and AI?
We can modify as needed but this gives us a starting point.

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• Why do organizations use staff augmentation and managed services?
• How do you differentiate and decide between filling roles that are internal, external consulting or managed services?
• What are some of the considerations that management focuses on when they build a plan to leverage managed services?
• What makes a good managed services partner?
• What are some of the risks of taking on a bad managed services partner?
• How do you incorporate a managed services provider into your IT management plan effectively?
• What is the best possible state for a managed services relationship to be in?
• How does the type of technology affect your decision making for how to support it?
• Where do you see managed services and consulting providing value to your team in the future?

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